Literature DB >> 25654782

Initial phantom study comparing image quality in computed tomography using adaptive statistical iterative reconstruction and new adaptive statistical iterative reconstruction v.

Kyungjae Lim1, Heejin Kwon, Jinhan Cho, Jongyoung Oh, Seongkuk Yoon, Myungjin Kang, Dongho Ha, Jinhwa Lee, Eunju Kang.   

Abstract

PURPOSE: The purpose of this study was to assess the image quality of a novel advanced iterative reconstruction (IR) method called as "adaptive statistical IR V" (ASIR-V) by comparing the image noise, contrast-to-noise ratio (CNR), and spatial resolution from those of filtered back projection (FBP) and adaptive statistical IR (ASIR) on computed tomography (CT) phantom image.
MATERIALS AND METHODS: We performed CT scans at 5 different tube currents (50, 70, 100, 150, and 200 mA) using 3 types of CT phantoms. Scanned images were subsequently reconstructed in 7 different scan settings, such as FBP, and 3 levels of ASIR and ASIR-V (30%, 50%, and 70%). The image noise was measured in the first study using body phantom. The CNR was measured in the second study using contrast phantom and the spatial resolutions were measured in the third study using a high-resolution phantom. We compared the image noise, CNR, and spatial resolution among the 7 reconstructed image scan settings to determine whether noise reduction, high CNR, and high spatial resolution could be achieved at ASIR-V.
RESULTS: At quantitative analysis of the first and second studies, it showed that the images reconstructed using ASIR-V had reduced image noise and improved CNR compared with those of FBP and ASIR (P < 0.001). At qualitative analysis of the third study, it also showed that the images reconstructed using ASIR-V had significantly improved spatial resolution than those of FBP and ASIR (P < 0.001).
CONCLUSIONS: Our phantom studies showed that ASIR-V provides a significant reduction in image noise and a significant improvement in CNR as well as spatial resolution. Therefore, this technique has the potential to reduce the radiation dose further without compromising image quality.

Mesh:

Year:  2015        PMID: 25654782     DOI: 10.1097/RCT.0000000000000216

Source DB:  PubMed          Journal:  J Comput Assist Tomogr        ISSN: 0363-8715            Impact factor:   1.826


  19 in total

1.  The adaptive statistical iterative reconstruction-V technique for radiation dose reduction in abdominal CT: comparison with the adaptive statistical iterative reconstruction technique.

Authors:  Heejin Kwon; Jinhan Cho; Jongyeong Oh; Dongwon Kim; Junghyun Cho; Sanghyun Kim; Sangyun Lee; Jihyun Lee
Journal:  Br J Radiol       Date:  2015-08-03       Impact factor: 3.039

2.  Potential value of the PixelShine deep learning algorithm for increasing quality of 70 kVp+ASiR-V reconstruction pelvic arterial phase CT images.

Authors:  Shi-Feng Tian; Ai-Lian Liu; Jing-Hong Liu; Yi-Jun Liu; Ju-Dong Pan
Journal:  Jpn J Radiol       Date:  2018-12-06       Impact factor: 2.374

3.  Sinogram-based deep learning image reconstruction technique in abdominal CT: image quality considerations.

Authors:  Anushri Parakh; Jinjin Cao; Theodore T Pierce; Michael A Blake; Cristy A Savage; Avinash R Kambadakone
Journal:  Eur Radiol       Date:  2021-04-23       Impact factor: 5.315

4.  Image quality comparison of two adaptive statistical iterative reconstruction (ASiR, ASiR-V) algorithms and filtered back projection in routine liver CT.

Authors:  Li-Hong Chen; Chao Jin; Jian-Ying Li; Ge-Liang Wang; Yong-Jun Jia; Hai-Feng Duan; Ning Pan; Jianxin Guo
Journal:  Br J Radiol       Date:  2018-06-06       Impact factor: 3.039

5.  Computed Tomography Image Quality Evaluation of a New Iterative Reconstruction Algorithm in the Abdomen (Adaptive Statistical Iterative Reconstruction-V) a Comparison With Model-Based Iterative Reconstruction, Adaptive Statistical Iterative Reconstruction, and Filtered Back Projection Reconstructions.

Authors:  Martin H Goodenberger; Nicolaus A Wagner-Bartak; Shiva Gupta; Xinming Liu; Ramon Q Yap; Jia Sun; Eric P Tamm; Corey T Jensen
Journal:  J Comput Assist Tomogr       Date:  2018 Mar/Apr       Impact factor: 1.826

6.  Low-dose CT angiography using ASiR-V for potential living renal donors: a prospective analysis of image quality and diagnostic accuracy.

Authors:  Woong Kyu Han; Joon Chae Na; Sung Yoon Park
Journal:  Eur Radiol       Date:  2019-08-30       Impact factor: 5.315

7.  Assessment of noise reduction potential and image quality improvement of a new generation adaptive statistical iterative reconstruction (ASIR-V) in chest CT.

Authors:  Hui Tang; Nan Yu; Yongjun Jia; Yong Yu; Haifeng Duan; Dong Han; Guangming Ma; Chenglong Ren; Taiping He
Journal:  Br J Radiol       Date:  2017-11-16       Impact factor: 3.039

8.  Head CT: Image quality improvement with ASIR-V using a reduced radiation dose protocol for children.

Authors:  Hyun Gi Kim; Ho-Joon Lee; Seung-Koo Lee; Hyun Ji Kim; Myung-Joon Kim
Journal:  Eur Radiol       Date:  2017-01-23       Impact factor: 5.315

9.  Detection of Colorectal Hepatic Metastases Is Superior at Standard Radiation Dose CT versus Reduced Dose CT.

Authors:  Corey T Jensen; Nicolaus A Wagner-Bartak; Lan N Vu; Xinming Liu; Bharat Raval; David Martinez; Wei Wei; Yuan Cheng; Ehsan Samei; Shiva Gupta
Journal:  Radiology       Date:  2018-11-27       Impact factor: 11.105

10.  Clinical value of a new generation adaptive statistical iterative reconstruction (ASIR-V) in the diagnosis of pulmonary nodule in low-dose chest CT.

Authors:  Hui Tang; Zhentang Liu; Zhijun Hu; Taiping He; Dou Li; Nan Yu; Yongjun Jia; Hong Shi
Journal:  Br J Radiol       Date:  2019-09-06       Impact factor: 3.039

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